We use this framework to explore when individuals can be expected to engage in selfish learning, which is the baseline learning paradigm in the evolutionary game theory literature. By considering the alternative learning rule FMTL as an example, we show that selfish learning can be dominated in many of the key examples studied in the literature, like the prisoner's dilemma or the stag-hunt game. In those games, FMTL (and presumably many other learning rules) prove more successful. These results indicate that even if individuals are ultimately driven by their own advantage, optimal learning rules may need to take into account additional aspects, such as the well-being of other group members. More broadly, we show that once learning rules themselves are subject to evolutionary pressures, individuals readily adapt to avoid many of the inefficiencies we observe in social interactions. To the extent that evolutionary models based on selfish learning have been used to make predictions for behavioral experiments [1]}, [2]}, [3]}, our results thus suggest that these models may underestimate how often humans learn to cooperate. Repeated two-player games clearly cannot capture all realistic interactions in which learning is relevant. However, such simple models can help to understand the principles at work in more complex settings [4]}. Delineating which aspects are crucial for successful learning is in our view one of the most exciting directions for future research.
